Impact of baby-friendly hospital initiatives on breastfeeding outcomes: Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The Baby-Friendly Hospital Initiative (BFHI) is a global effort promoting the Ten Steps to support breastfeeding in maternity-care facilities. AIM: This study examined the effect of BFHI on breastfeeding outcomes, focusing on initiation rates, exclusive breastfeeding durations, and factors influencing its effectiveness. METHODS: A systematic review and meta-analysis were conducted by searching nine databases (1991 to February 2024). Included studies were experimental, quasi-experimental, or observational studies, with sites implementing the full BFHI or at least three steps. Two reviewers independently screened studies, assessed risk of bias, and extracted data. Random-effects models were used for pooled results, with subgroup analyses based on BFHI status and country income level. DISCUSSION: Eighty-six studies were included. Infants in BFHI hospitals were more likely to be exclusively breastfed at ≤ 3 months (OR= 1.77; 95 % CI: 1.37-2.29) and 3-6 months (OR= 1.82; 95 % CI: 1.26-2.61). Higher rates of any breastfeeding were observed at ≤ 3 months (OR= 1.48; 95 % CI: 1.17-1.87), 3-6 months (OR= 1.75; 95 % CI: 1.18-2.61) and at > 6 months (OR= 2.34; 95 % CI: 1.04-5.27). CONCLUSIONS: BFHI implementation positively impacts breastfeeding outcomes, with both short- and long-term effects. Partial implementation also correlates with higher exclusive breastfeeding rates. Insignificant differences across income levels may reflect the limited number of studies in low-and middle- income countries. Further research with longer-term follow up is needed to confirm long-term effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".